The unintended consequences of COVID‐19 public health measures on health care for children with medical complexity
Bibliographic record
Abstract
AIM: The aim of this work is to explore the unintended consequences of pandemic public health measures on health care service usage by children with medical complexity. BACKGROUND: Medical complexity is characterized by the presence of complex, chronic conditions requiring specialized care, substantial health needs, functional dependence and/or limitations, and frequent health care usage. Children with medical complexity are among the highest users of paediatric health care services. METHODS: A web-based, cross-sectional survey was conducted in British Columbia, Canada, between August and September 2020. Inclusion criteria were (a) parent/guardian of at least one child (age 0 to 18 years, inclusive) with medical complexity and (b) residence in British Columbia. A convenience sample of 156 parents completed the survey. Data were analysed using a series of descriptive analyses (frequencies, cross-tabulations) and inferential analyses (binary logistic regressions). RESULTS: Respondents provided information for 188 children with medical complexity. Access to allied health therapies (physio, occupational, and speech and language) and medical specialists drastically declined in the initial months of the pandemic, with a shift from in-person to virtual platforms for these aspects of care. Regression modelling indicated that age and family structure influenced decisions to use in-patient hospital services. CONCLUSIONS: Public health measures implemented in the initial months of the pandemic decreased access to health care services for children with medical complexity. The long-term ramifications of these measures are unknown. Family structure was found to influence decisions to avoid accessing Emergency Department care. Given the volume of services used by these children, paediatric hospital leaders need to take their unique needs into consideration in disaster planning to ensure minimal disruptions in care.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".